Data Fusion applications for condition monitoring have been reported in academic literature, mainly in the field of electric motor diagnostics [1] and gearbox diagnostics [2]. In the first case, the diagnosis of an electric motor is conducted by fusing vibration and current signals, and in the case of the gearbox, it is carried out fusing vibration signals and debris analysis results. In neither case are the signals from the other components considered.
Similarly, Bayesian inference-based condition monitoring techniques have been developed previously. In Ref. [3] an expert system for vibration fault diagnosis is shown. The system makes use of a Bayesian algorithm to define probabilities that are included in a decision tree or in a decision table, and then, on the basis of observations, the expert system generates a diagnosis of the health of the system. It is important to note that only vibration signals are used in the construction of the decision tree or the decision table and that the method was used to diagnose only electric motors, not the entire drive-train.
Multi-sensor Data Fusion has been typically used in applications that make use of Neural Networks. In this case, the raw signals are fed to the neural network to train it. From patent description U.S. Pat. No. 7,539,549 there is known a system and a method in which a neural network is trained with multiple kinds of signals, and afterwards a Fuzzy Logic-based Expert System is used to diagnose a motor-pump arrangement. Due to the fact that such systems require large amount of data to be available in order to initially train the network, they are less than ideal. Furthermore, whilst neural networks are undoubtedly powerful, they represent black-boxes in that the underlying reasoning as to why a particular decision has been made is difficult to ascertain. By combining indicators (which in general are based on physical reasoning, e.g. using the kurtosis to identify the impulsive nature of a measured signal that is indicative of a localized gear tooth problem in a gearbox) with fault probabilities given by experts in the field, the approach given in this disclosure allows the reasoning for each and every decision to be rationalized fully.